def test_type_shape(self, input_data, expected_type, expected_count, expected_shape): results = [] for p in TEST_NDARRAYS + (None, ): input_data = deepcopy(input_data) if p is not None: for k in ["fg_indices", "bg_indices"]: input_data[k] = p(input_data[k]) set_determinism(0) result = generate_pos_neg_label_crop_centers(**input_data) self.assertIsInstance(result, expected_type) self.assertEqual(len(result), expected_count) self.assertEqual(len(result[0]), expected_shape) # check for consistency between numpy, torch and torch.cuda results.append(result) if len(results) > 1: # compare every crop center for x, y in zip(results[0], results[-1]): assert_allclose(x, y, type_test=False)
def test_type_shape(self, input_data, expected_type, expected_count, expected_shape): result = generate_pos_neg_label_crop_centers(**input_data) self.assertIsInstance(result, expected_type) self.assertEqual(len(result), expected_count) self.assertEqual(len(result[0]), expected_shape)